• Journal of Preventive Medicine and Public Health
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• v.55 no.6
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• pp.568-577
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• 2022

Objectives: Many studies have shown that social distancing, as a non-pharmaceutical intervention (NPI) that is one of the various measures against coronavirus disease 2019 (COVID-19), is an effective preventive measure to suppress the spread of infectious diseases. This study explored the relationships between traditional health-related behaviors in Korea and social distancing practices during the COVID-19 pandemic. Methods: Data were obtained from the 2020 Community Health Survey conducted by the Korea Disease Control and Prevention Agency (n=98 149). The dependent variable was the degree of social distancing practice to cope with the COVID-19 epidemic. Independent variables included health-risk behaviors and health-promoting behaviors. The moderators were vaccination and unmet medical needs. Predictors affecting the practice of social distancing were identified through hierarchical multiple logistic regression analysis. Results: Smokers (adjusted odds ratio [aOR], 0.924) and frequent drinkers (aOR, 0.933) were more likely not to practice social distancing. A greater degree of physical activity was associated with a higher likelihood of practicing social distancing (aOR, 1.029). People who were vaccinated against influenza were more likely to practice social distancing than those who were not (aOR, 1.150). However, people with unmet medical needs were less likely to practice social distancing than those who did not experience unmet medical needs (aOR, 0.757). Conclusions: Social distancing practices were related to traditional health behaviors such as smoking, drinking, and physical activity. Their patterns showed a clustering effect of health inequality. Therefore, when establishing a strategy to strengthen social distancing, a strategy to protect the vulnerable should be considered concomitantly.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.377-397
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• 2022

The number of public libraries in Korea has been increasing. However, the focus was on quantitative growth, while it did not have much interests in whether its growth trend are have deviations by region, and if that is a fact, what factors caused such a disparity. For this reason, this study analyzes spatial distribution of public libraries in Korea and its affecting factors of regional gap. As a result, public libraries are constantly distributing in the metropolitan area and the distribution of public libraries showed deviations by region. The results of analysis regarding the determinants of public libraries distribution, rate of population growth, the number of businesses and financial independence rate are found to have a positive effect but local taxes per capita are not. Especially economic power of region and financial ability of a local government are key factors of regional disparity. It shows empirically that the supply of public libraries has been determined by the convenience of suppliers.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.343-349
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• 2022

This paper proposes a new topology optimization scheme to determine optimized joints for multi-component models. The joints are modeled as zero-length high-stiffness spring elements. The spring joints are considered as mesh-independent springs based on a joint-element interpolation scheme. This enables the changing of the location of the joints regardless of the connected nodes during optimization. Because the joints are movable, the locations of the optimized joints should be aggregated at several points. In this paper, the novel joint dispersal (JD) constraint to prevent joint clustering is proposed. With the joint dispersal constraint, it is possible to determine the optimized joint location as well as optimized topologies while maintaining the minimum distance between each joint. The mechanical compliance value is considered as the objective function. Several topology optimization examples are solved to demonstrate the effect of the joint dispersal constraint.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.295-307
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• 2023

The heterogeneous autoregressive (HAR) model is a simple linear model that is commonly used to explain long memory in the realized volatility. However, as realized volatility has more complicated features such as conditional heteroscedasticity, leverage effect, and volatility clustering, it is necessary to extend the simple HAR model. Therefore, to better incorporate the stylized facts, we propose a threshold HAR model with GARCH errors, namely the THAR-GARCH model. That is, the THAR-GARCH model is a nonlinear model whose coefficients vary according to a threshold value, and the conditional heteroscedasticity is explained through the GARCH errors. Model parameters are estimated using an iterative weighted least squares estimation method. Our simulation study supports the consistency of the iterative estimation method. In addition, we show that the proposed THAR-GARCH model has better forecasting power by applying to the realized volatility of major 21 stock indices around the world.

• Journal of Ecology and Environment
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• v.48 no.2
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• pp.120-127
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• 2024

Bats serve as vectors and natural reservoir hosts for various infectious viruses, bacteria, and fungi. These pathogens have also been detected in bat feces and can cause severe illnesses in hosts, other animals, and humans. Because pathogens can easily spread into the environment through bat feces, determining the bacterial communities in bat guano is crucial to mitigate potential disease transmission and outbreaks. This study primarily aimed to examine bacterial communities in the feces of insectivorous bats living in South Korea. Fecal samples were collected after capturing 84 individuals of four different bat species in two regions of South Korea, and the bacterial microbiota was assessed through next generation sequencing of the 16S rRNA gene. The results revealed that, with respect to the relative abundance at the phylum level, Myotis bombinus was dominated by Firmicutes (47.24%) and Proteobacteria (42.66%) whereas Miniopterus fuliginosus (82.78%), Rhinolophus ferrumequinum (63.46%), and Myotis macrodactylus (78.04%) were dominated by Proteobacteria. Alpha diversity analysis showed no difference in abundance between species and a significant difference (p < 0.05) between M. bombinus and M. fuliginosus. Beta-diversity analysis revealed that Clostridium, Asaia, and Enterobacteriaceae_g were clustered as major factors at the genus level using principal component analysis. Additionally, linear discriminant analysis effect size was conducted based on relative expression information to select bacterial markers for each bat species. Clostridium was relatively abundant in M. bombinus, whereas Mycoplasma_g10 was relatively abundant in R. ferrumequinum. Our results provide an overview of bat guano microbiota diversity and the significance of pathogenic taxa for humans and the environment, highlighting a better understanding of preventing emerging diseases. We anticipate that this research will yield bioinformatic data to advance our knowledge of overall microbial genetic diversity and clustering characteristics in insectivorous bat feces in South Korea.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.2
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• pp.77-86
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• 2017

Selection of location in food service start-up business is sure to be a shortcut to achievement of business performance, and in this context, it's no exaggeration to say that food service industry is an real estate industry. This study looked into what influence of the location factor in food service start-up business had on sales performance, and intended to verify whether the location factors ultimately influenced business performance consequent on the influence of location factors on start-up business satisfaction. To this end, this study set food service owner-operators as its research subject, and conducted a survey of the operators (of restaurants) located in Anyang-si from December 1, 2016 until January 30, 2017. This study distributed a total of 300 copies of questionnaires, and collected 245 copes, among which this study used 198 copies for empirical study excluding the copies whose reply was unfaithful. This study did empirical analysis of 198 copies using SPSS 22.0 Statistical Package Program, together with the application of frequency analysis, factor analysis and regression analysis. The major results of this study are as follows: First, this study divided the location factors in food service start-up business stores into the four, i.e. accessibility, clustering property, placeness and visibility, etc. Second, the study results showed that accessibility, clustering property, placeness and visibility had significant influence as one in the influence of locational factors on sales performance. Third, this study could understand that start-up business satisfaction had a partial mediated effect in the influence of location factors on sales performance. Resultantly, this study confirmed food service start-up business's own selection of location, and wished to find major factors and a differentiated point in time of selection of location of stores in other fields. Such a result gives an implication that it's necessary to concentrate all efforts to increase sales performance of food service start-up business from the location selection phase, and to make efforts to increase start-up business satisfaction.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.171-191
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• 2020

This study focused on digital music, which is the most valuable cultural asset in the modern society and occupies a particularly important position in the flow of the Korean Wave. Digital music was collected based on the "Gaon Chart," a well-established music chart in Korea. Through this, the changes in the ranking of the music that entered the chart for 73 weeks were collected. Afterwards, patterns with similar characteristics were derived through time series cluster analysis. Then, a descriptive analysis was performed on the notable features of each pattern. The research process suggested by this study is as follows. First, in the data collection process, time series data was collected to check the ranking change of digital music. Subsequently, in the data processing stage, the collected data was matched with the rankings over time, and the music title and artist name were processed. Each analysis is then sequentially performed in two stages consisting of exploratory analysis and explanatory analysis. First, the data collection period was limited to the period before 'the music bulk buying phenomenon', a reliability issue related to music ranking in Korea. Specifically, it is 73 weeks starting from December 31, 2017 to January 06, 2018 as the first week, and from May 19, 2019 to May 25, 2019. And the analysis targets were limited to digital music released in Korea. In particular, digital music was collected based on the "Gaon Chart", a well-known music chart in Korea. Unlike private music charts that are being serviced in Korea, Gaon Charts are charts approved by government agencies and have basic reliability. Therefore, it can be considered that it has more public confidence than the ranking information provided by other services. The contents of the collected data are as follows. Data on the period and ranking, the name of the music, the name of the artist, the name of the album, the Gaon index, the production company, and the distribution company were collected for the music that entered the top 100 on the music chart within the collection period. Through data collection, 7,300 music, which were included in the top 100 on the music chart, were identified for a total of 73 weeks. On the other hand, in the case of digital music, since the cases included in the music chart for more than two weeks are frequent, the duplication of music is removed through the pre-processing process. For duplicate music, the number and location of the duplicated music were checked through the duplicate check function, and then deleted to form data for analysis. Through this, a list of 742 unique music for analysis among the 7,300-music data in advance was secured. A total of 742 songs were secured through previous data collection and pre-processing. In addition, a total of 16 patterns were derived through time series cluster analysis on the ranking change. Based on the patterns derived after that, two representative patterns were identified: 'Steady Seller' and 'One-Hit Wonder'. Furthermore, the two patterns were subdivided into five patterns in consideration of the survival period of the music and the music ranking. The important characteristics of each pattern are as follows. First, the artist's superstar effect and bandwagon effect were strong in the one-hit wonder-type pattern. Therefore, when consumers choose a digital music, they are strongly influenced by the superstar effect and the bandwagon effect. Second, through the Steady Seller pattern, we confirmed the music that have been chosen by consumers for a very long time. In addition, we checked the patterns of the most selected music through consumer needs. Contrary to popular belief, the steady seller: mid-term pattern, not the one-hit wonder pattern, received the most choices from consumers. Particularly noteworthy is that the 'Climbing the Chart' phenomenon, which is contrary to the existing pattern, was confirmed through the steady-seller pattern. This study focuses on the change in the ranking of music over time, a field that has been relatively alienated centering on digital music. In addition, a new approach to music research was attempted by subdividing the pattern of ranking change rather than predicting the success and ranking of music.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.87-93
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• 2009

Wireless mesh networks (WMNs) are emerging technologies that provide ubiquitous environments and wireless broadband access. The aggregate capacity of WMNs can be improved by minimizing the effect of channel interference. The IEEE 802.11b/g standard which is mainly used for the network interface technology in WMNs provides 3 multiple channels. We must consider the channel scanning delay and the channel dependency problem to effectively assign channels in like these multi-channel WMNs. This paper proposes a cluster-based channel assignment (CB-CA) algorithm for multi-channel WMNs to solve such problems. The CB-CA does not perform the channel scanning and the channel switching through assigning co-channel to the inter-cluster head (CH) links. In the CB-CA, the communication between the CH and cluster member (CM) nodes uses a channel has no effect on channels being used by the inter-CH links. Therefore, the CB-CA can minimize the interference within multi-channel environments. Our simulation results show that CB-CA can improve the performance of WMNs.

• Atmosphere
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• v.22 no.2
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• pp.149-161
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• 2012

Going by the research results of the past, of all the uncertainties resulting from the research on climate change, the uncertainty caused by the climate change scenario has the highest degree of uncertainty. Therefore, depending upon what kind of climate change scenario one adopts, the projection of the water resources in the future will differ significantly. As a matter of principle, it is highly recommended to utilize all the GCM scenarios offered by the IPCC. However, this could be considered to be an impractical alternative if a decision has to be made at an action officer's level. Hence, as an alternative, it is deemed necessary to select several scenarios so as to express the possible number of cases to the maximum extent possible. The objective standards in selecting the climate change scenarios have not been properly established and the scenarios have been selected, either at random or subject to the researcher's discretion. In this research, a new scenario selection process, in which it is possible to have the effect of having utilized all the possible scenarios, with using only a few principal scenarios and maintaining some of the uncertainties, has been suggested. In this research, the use of cluster analysis and the selection of a representative scenario in each cluster have efficiently reduced the number of climate change scenarios. In the cluster analysis method, the K-means clustering method, which takes advantage of the statistical features of scenarios has been employed; in the selection of a representative scenario in each cluster, the selection method was analyzed and reviewed and the PDF method was used to select the best scenarios with the closest simulation accuracy and the principal scenarios that is suggested by this research. In the selection of the best scenarios, it has been shown that the GCM scenario which demonstrated high level of simulation accuracy in the past need not necessarily demonstrate the similarly high level of simulation accuracy in the future and various GCM scenarios were selected for the principal scenarios. Secondly, the "Maximum entropy" which can quantify the uncertainties of the climate change scenario has been used to both quantify and compare the uncertainties associated with all the scenarios, best scenarios and the principal scenarios. Comparison has shown that the principal scenarios do maintain and are able to better explain the uncertainties of all the scenarios than the best scenarios. Therefore, through the scenario selection process, it has been proven that the principal scenarios have the effect of having utilized all the scenarios and retaining the uncertainties associated with the climate change to the maximum extent possible, while reducing the number of scenarios at the same time. Lastly, the climate change scenario most suitable for the climate on the Korean peninsula has been suggested. Through the scenario selection process, of all the scenarios found in the 4th IPCC report, principal climate change scenarios, which are suitable for the Korean peninsula and maintain most of the uncertainties, have been suggested. Therefore, it is assessed that the use of the scenario most suitable for the future projection of water resources on the Korean peninsula will be able to provide the projection of the water resources management that maintains more than 70~80% level of uncertainties of all the scenarios.